Optimal load scheduling of plug-in hybrid electric vehicles viaweight-aggregation multi-objective evolutionary algorithms
Optimal load scheduling of plug-in hybrid electric vehicles viaweight-aggregation multi-objective evolutionary algorithms
复制标题
基于权重聚合多目标进化算法的插电式混合动力汽车最优负载调度
DOI：
10.1109/tits.2016.2638898
复制
发表时间：
2017
影响因子：
8.5
通讯作者：
Khaled Sedraoui
中科院分区：
文献类型：
--
作者：
Qi Kang;Shuwei Feng;MengChu Zhou;Ahmed C. Ammari;Khaled Sedraoui
为了保护环境和减缓全球变暖的趋势，许多政府和环保人士都热衷于推广使用插电式混合动力电动汽车（PHEV）。因此，越来越多的PHEV被投入使用。然而，由它们的无序充电引起的负荷峰值可能对整个电网有害。已经提出了几种方法来建立有序的PHEV充电。但这些方法都是针对单目标的负荷调度，不能满足真实的要求，需要进行多目标优化。本文提出了一个多目标负荷调度问题，以最小化两个相互竞争的目标：1）潜在的严重峰谷差和2）经济损失。当我们应用现有的多目标进化算法（MOEAs），即，多目标粒子群优化算法（MOPSO）、非支配排序遗传算法II、基于分解的MOEA、多目标差分进化算法等来解决该问题，但由于其维数较高和条件特殊，我们发现它们都不能达到Pareto前沿或只能收敛到一个相对较小的区域。因此，我们提出了一个权重聚合（WA）的策略，并实现了一个新的MOEA算法命名为WA-MOPSO的结合WA到MOPSO来解决这个问题。它的有效性和效率，以产生一个帕累托前沿这个问题进行了验证，并与那些国家的最先进的方法进行比较。此外，WA还结合其他MOEA解决定义的调度问题。
In order to protect the environment and slow down global warming trend, many governments and environmentalists are keen at promoting the use of plug-in hybrid electric vehicles (PHEVs). As a result, more and more PHEVs have been put into use. However, load peak caused by their disordered charging can be detrimental to an entire power grid. Several methods have been proposed to establish ordered PHEV charging. While focusing on single-objective load scheduling, they fail to meet the real requirements that need one to conduct multiple objective optimization. This paper formulates a multi-objective load scheduling problem to minimize two competing objectives: 1) potential serious peak-to-valley difference and 2) economic loss. When we apply existing multi-objective evolutionary algorithms (MOEAs), i.e., multi-objective particle swarm optimization (MOPSO), Nondominated Sorting Genetic Algorithm II, MOEA based on decomposition, and multi-objective differential evolutionary algorithm to solve it, because its high dimension and special conditions we find that they fail to reach the Pareto Front or converge into a relatively small area only. Therefore, we propose a weight aggregation (WA) strategy and implement a novel MOEA algorithm named WA-MOPSO by incorporating WA into MOPSO to solve the problem. Its effectiveness and efficiency to generate a Pareto front of this problem are verified and compared with those of the state-of-the-art approaches. Furthermore, WA is also combined with other MOEAs to solve the defined scheduling problem.